Updated: 2020-08-03 06:20:08 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from log_2(R_e) > 0 to log_2(R_e) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

This is the same computation at a County level


state R_e cases daily_cases
Massachusetts 1.23 118108 433
Montana 1.18 4159 137
South Dakota 1.18 8779 86
Illinois 1.12 182522 1621
Missouri 1.12 46870 1246
Nebraska 1.12 26628 318
Maryland 1.11 90846 982
Oklahoma 1.11 38713 1075
West Virginia 1.11 6873 144
Georgia 1.10 175999 3686
Tennessee 1.10 107651 2478
Maine 1.08 3959 22
Minnesota 1.07 55887 750
Wisconsin 1.07 55155 961
Alabama 1.06 91372 1832
Iowa 1.06 45860 527
Kentucky 1.06 32227 659
Michigan 1.06 92137 784
Oregon 1.06 19180 349
Indiana 1.05 69659 885
Nevada 1.05 50326 1104
Rhode Island 1.05 17347 83
Arkansas 1.04 42479 767
Mississippi 1.04 61190 1344
New Jersey 1.04 183682 415
Arizona 1.02 179004 2576
North Dakota 1.02 6658 121
Ohio 1.02 93519 1354
Texas 1.02 455961 8487
North Carolina 1.01 125941 1880
South Carolina 1.00 92136 1582
New Hampshire 0.99 6649 30
New York 0.99 421112 670
Wyoming 0.99 2817 48
Washington 0.98 60420 804
Idaho 0.97 21701 469
Virginia 0.97 73493 770
Florida 0.96 489184 9650
Pennsylvania 0.96 118417 878
Colorado 0.94 47934 522
Connecticut 0.93 49726 136
California 0.92 518596 8278
New Mexico 0.91 21146 273
Delaware 0.89 14747 88
Kansas 0.89 28435 362
Utah 0.89 41186 457
Louisiana 0.88 119494 1705
Vermont 0.79 1426 4

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

## Warning: Removed 1 rows containing missing values (position_stack).
## Warning: Removed 1 row(s) containing missing values (geom_path).

Mortality Trend

National \(R_e\)

There is also large variation in the distribution of \(R_e\) values. This shows how that distribution has changed over the last three weeks. As a reminder, for disease reduction, \(R_e\) needs to be sustained below 1.0.

Trend

Distribution of \(R_e\) Values

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
Pierce WA 4 1 1.1 5549 650 107
King WA 1 2 0.9 15563 720 163
Chelan WA 10 3 1.2 1134 1500 34
Spokane WA 5 4 1.0 3866 780 83
Douglas WA 13 5 1.2 809 1960 27
Stevens WA 28 6 1.4 93 210 6
Yakima WA 2 7 0.9 10584 4250 67
Snohomish WA 3 8 0.9 5804 740 56
Franklin WA 7 9 0.9 3461 3820 40
Benton WA 6 12 0.8 3710 1910 44
Grant WA 9 13 1.0 1318 1390 23
Clark WA 8 22 0.7 1800 390 17
OR
county ST case rank severity R_e cases cases/100k daily cases
Yamhill OR 12 1 1.5 360 350 14
Jackson OR 11 2 1.4 384 180 16
Multnomah OR 1 3 1.0 4473 560 73
Washington OR 2 4 1.1 2836 490 50
Marion OR 3 5 1.0 2659 790 39
Malheur OR 6 6 1.2 676 2220 15
Morrow OR 14 7 1.2 306 2730 10
Umatilla OR 4 8 0.9 2030 2640 42
Clackamas OR 5 9 1.0 1400 350 20
Lane OR 8 11 1.1 520 140 11
Deschutes OR 7 12 0.9 544 300 12
Lincoln OR 9 21 0.9 393 820 2
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.0 193184 1910 2687
Fresno CA 7 2 1.1 15504 1590 400
Santa Clara CA 10 3 1.2 10518 550 240
San Francisco CA 17 4 1.2 6831 790 146
Sonoma CA 25 5 1.3 3116 620 91
Monterey CA 21 6 1.2 4813 1110 129
Ventura CA 16 7 1.1 7724 910 167
San Diego CA 5 8 0.9 30022 910 415
Orange CA 3 9 0.9 37292 1180 465
Alameda CA 9 10 1.0 11663 710 184
San Bernardino CA 4 11 0.8 33402 1560 540
Riverside CA 2 13 0.8 38294 1610 456
Kern CA 6 17 0.7 21035 2380 576
San Joaquin CA 8 26 0.7 11871 1620 183

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 1.0 120682 2840 1827
Pima AZ 2 2 1.2 16680 1640 270
Pinal AZ 4 3 1.0 8242 1960 120
Yuma AZ 3 4 0.9 11180 5380 128
Yavapai AZ 10 5 1.1 1822 810 36
Cochise AZ 11 6 1.1 1543 1220 24
Gila AZ 12 7 1.1 842 1580 23
Apache AZ 6 8 1.0 3059 4280 24
Navajo AZ 5 10 0.9 5272 4850 31
Mohave AZ 7 11 0.8 3047 1480 42
Coconino AZ 8 12 0.8 2989 2130 19
Santa Cruz AZ 9 13 0.7 2622 5630 15
CO
county ST case rank severity R_e cases cases/100k daily cases
Adams CO 3 1 1.1 6098 1230 77
Arapahoe CO 2 2 1.0 7014 1100 70
Summit CO 16 3 1.5 337 1110 4
Broomfield CO 15 4 1.4 411 620 6
El Paso CO 4 5 0.9 4700 680 71
Denver CO 1 6 0.8 9804 1410 86
Boulder CO 7 7 1.1 1933 600 22
Jefferson CO 5 8 0.9 3964 690 46
Larimer CO 9 9 0.9 1402 410 22
Douglas CO 8 13 0.8 1652 500 19
Weld CO 6 17 0.8 3553 1200 18
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 0.9 19463 1740 189
Utah UT 2 2 0.9 7936 1340 110
Davis UT 3 3 0.9 3006 880 45
Weber UT 4 4 0.9 2603 1050 40
Box Elder UT 12 5 1.0 329 620 6
Washington UT 5 6 0.7 2316 1440 21
Cache UT 6 7 0.8 1828 1490 10
San Juan UT 8 9 0.8 626 4100 6
Tooele UT 9 10 0.7 542 830 6
Summit UT 7 11 0.8 696 1720 4
NM
county ST case rank severity R_e cases cases/100k daily cases
Cibola NM 7 1 1.3 665 2460 40
Chaves NM 12 2 1.4 363 550 16
Curry NM 10 3 1.2 481 960 16
Santa Fe NM 9 4 1.2 594 400 15
Bernalillo NM 1 5 0.8 4881 720 66
Sandoval NM 5 6 1.0 1097 780 13
Doña Ana NM 4 7 0.8 2237 1040 29
San Juan NM 3 8 0.9 3012 2360 12
Lea NM 8 9 0.8 650 930 16
McKinley NM 2 10 0.7 4013 5510 14
Otero NM 6 18 0.4 1086 1650 3

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Cumberland NJ 15 1 1.4 3255 2120 17
Passaic NJ 5 2 1.3 17638 3500 29
Burlington NJ 12 3 1.2 5882 1320 34
Hudson NJ 3 4 1.2 19683 2940 22
Monmouth NJ 8 5 1.1 10226 1640 38
Bergen NJ 1 6 1.0 20790 2240 40
Camden NJ 9 7 1.0 8430 1660 42
Essex NJ 2 8 1.1 19782 2490 32
Ocean NJ 7 9 1.0 10543 1780 37
Middlesex NJ 4 14 0.7 17930 2170 26
Union NJ 6 16 0.8 16750 3030 6
PA
county ST case rank severity R_e cases cases/100k daily cases
Union PA 41 1 2.4 165 370 10
Lancaster PA 6 2 1.3 5639 1050 54
Luzerne PA 11 3 1.4 3311 1040 26
Fayette PA 28 4 1.5 412 310 16
Allegheny PA 4 5 1.0 8302 680 142
Erie PA 21 6 1.4 964 350 18
Delaware PA 3 7 1.0 8834 1570 69
Bucks PA 5 8 1.0 6995 1120 46
Montgomery PA 2 10 0.9 9859 1200 49
Philadelphia PA 1 11 0.7 30498 1940 112
Lehigh PA 9 15 1.0 4856 1340 23
Berks PA 7 17 0.9 5197 1250 24
Chester PA 8 24 0.7 4928 950 33
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore city MD 4 1 1.2 11584 1880 185
Prince George’s MD 1 2 1.1 23202 2560 176
Baltimore MD 3 3 1.1 12210 1480 189
Anne Arundel MD 5 4 1.2 6952 1220 86
Calvert MD 15 5 1.5 618 680 14
Montgomery MD 2 6 1.1 17804 1710 110
Howard MD 6 7 1.1 3618 1150 42
Harford MD 9 8 1.1 1819 720 34
Charles MD 8 11 1.1 1880 1190 21
Frederick MD 7 17 0.7 3017 1210 15
VA
county ST case rank severity R_e cases cases/100k daily cases
Prince Edward VA 27 1 2.0 352 1530 17
Henry VA 24 2 1.6 508 980 13
Bedford VA 37 3 1.4 302 390 12
Chesterfield VA 5 4 1.1 4015 1180 44
Virginia Beach city VA 4 5 0.9 4324 960 112
Prince William VA 2 6 1.0 8945 1960 59
Norfolk city VA 7 7 0.9 3265 1330 75
Henrico VA 6 9 1.0 3636 1120 45
Arlington VA 8 10 1.2 2925 1260 17
Fairfax VA 1 11 0.9 15766 1380 56
Newport News city VA 9 22 0.8 1635 910 29
Loudoun VA 3 23 0.8 5049 1310 24
WV
county ST case rank severity R_e cases cases/100k daily cases
Mercer WV 14 1 1.9 161 270 13
Logan WV 16 2 1.5 151 450 12
Raleigh WV 10 3 1.5 189 250 10
Grant WV 24 4 1.3 80 690 6
Kanawha WV 2 5 1.0 829 450 22
Monongalia WV 1 6 1.0 918 870 14
Harrison WV 9 7 1.2 193 280 5
Cabell WV 4 8 1.0 332 350 8
Berkeley WV 3 9 0.9 658 580 6
Ohio WV 6 19 0.5 255 600 3
Wood WV 7 21 0.6 237 280 1
Jefferson WV 5 22 0.5 290 520 1
Randolph WV 8 26 0.2 208 720 0
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 0.9 6896 1240 56
Sussex DE 2 2 0.8 5654 2580 20
Kent DE 3 3 0.8 2197 1260 13

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Mobile AL 2 1 1.5 8822 2130 288
Jackson AL 30 2 1.7 817 1570 29
Calhoun AL 14 3 1.3 1575 1370 75
Jefferson AL 1 4 1.0 11856 1800 232
Madison AL 4 5 1.0 5036 1410 108
Colbert AL 23 6 1.2 1100 2020 30
DeKalb AL 12 7 1.1 1714 2410 33
Montgomery AL 3 11 0.9 6290 2770 78
Marshall AL 8 12 1.0 2977 3130 44
Shelby AL 7 16 0.9 3170 1500 61
Tuscaloosa AL 5 17 0.9 3995 1940 57
Baldwin AL 6 22 0.8 3245 1560 76
Lee AL 9 23 0.9 2640 1660 38
MS
county ST case rank severity R_e cases cases/100k daily cases
George MS 37 1 2.2 600 2530 67
Tallahatchie MS 46 2 2.0 468 3260 26
Sharkey MS 77 3 2.0 181 4010 16
DeSoto MS 2 4 1.4 3261 1850 88
Tunica MS 66 5 1.7 262 2580 14
Monroe MS 33 6 1.3 702 1960 18
Lee MS 11 7 1.2 1231 1450 38
Forrest MS 8 12 1.1 1641 2170 36
Hinds MS 1 13 0.9 5284 2190 105
Jones MS 7 16 1.1 1770 2590 32
Harrison MS 5 21 0.9 2121 1050 46
Washington MS 9 22 1.0 1484 3150 34
Rankin MS 4 27 0.8 2163 1430 45
Jackson MS 6 28 0.8 1947 1370 51
Madison MS 3 37 0.8 2308 2230 33
LA
county ST case rank severity R_e cases cases/100k daily cases
East Baton Rouge LA 2 1 1.0 11090 2500 200
Union LA 41 2 1.5 613 2730 12
Calcasieu LA 4 3 1.0 6471 3230 157
Jefferson LA 1 4 0.9 14418 3310 136
Caddo LA 6 5 0.9 6279 2530 90
Franklin LA 31 6 1.2 816 4020 17
Ascension LA 14 7 1.0 2566 2120 46
St. Tammany LA 7 9 0.9 4750 1880 67
Ouachita LA 8 10 0.9 4474 2870 58
Orleans LA 3 14 0.8 10380 2660 64
Lafayette LA 5 15 0.8 6325 2630 79
Tangipahoa LA 9 21 0.8 3130 2400 44

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Jefferson FL 62 1 2.7 324 2300 39
Liberty FL 55 2 2.1 405 4840 15
Marion FL 19 3 1.4 5355 1540 299
Miami-Dade FL 1 4 1.0 123873 4560 2757
Broward FL 2 5 1.0 58157 3050 1306
Gulf FL 57 6 1.7 388 2420 28
Franklin FL 66 7 1.9 128 1090 9
Hillsborough FL 4 9 1.0 30093 2180 455
Palm Beach FL 3 10 0.9 34431 2380 580
Pinellas FL 7 11 1.0 16816 1760 245
Orange FL 5 16 0.8 29843 2260 403
Polk FL 9 19 0.9 12991 1940 228
Duval FL 6 22 0.8 21678 2350 292
Lee FL 8 26 0.9 15723 2190 189
GA
county ST case rank severity R_e cases cases/100k daily cases
Chattahoochee GA 49 1 2.6 659 6120 29
Gwinnett GA 2 2 1.2 17821 1980 387
Richmond GA 9 3 1.3 3738 1860 146
Cobb GA 4 4 1.0 11685 1570 246
Hall GA 5 5 1.2 5548 2830 100
Columbia GA 20 6 1.3 1921 1300 66
Walton GA 41 7 1.4 962 1070 33
Fulton GA 1 9 0.9 18231 1780 338
DeKalb GA 3 10 1.0 12526 1690 206
Muscogee GA 8 27 1.0 4328 2200 70
Chatham GA 6 37 0.9 5010 1750 102
Clayton GA 7 38 0.9 4553 1630 77

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Cameron TX 9 1 1.7 11876 2820 705
Live Oak TX 114 2 2.3 224 1850 21
Taylor TX 36 3 1.8 1861 1360 112
Harris TX 1 4 1.2 76367 1660 1792
Maverick TX 35 5 1.7 1889 3260 91
Midland TX 34 6 1.7 2098 1280 72
Nueces TX 8 7 1.3 12666 3510 354
Tarrant TX 4 9 1.0 29329 1450 557
Dallas TX 2 12 0.9 51269 1980 653
Bexar TX 3 14 0.8 41820 2170 708
Travis TX 5 15 1.0 21205 1760 246
El Paso TX 7 16 1.0 14681 1750 225
Hidalgo TX 6 34 0.7 17371 2050 251
OK
county ST case rank severity R_e cases cases/100k daily cases
Le Flore OK 30 1 1.9 246 490 21
Oklahoma OK 1 2 1.1 9436 1210 259
Cleveland OK 3 3 1.3 2736 990 99
Tulsa OK 2 4 1.0 9184 1430 222
Sequoyah OK 29 5 1.5 266 640 19
Canadian OK 4 6 1.3 1091 800 39
Cherokee OK 25 7 1.4 345 710 19
Wagoner OK 9 8 1.2 742 950 25
Rogers OK 7 13 1.0 816 900 28
Comanche OK 8 28 0.9 782 640 13
Texas OK 5 42 0.9 1037 4910 2
McCurtain OK 6 47 0.5 830 2520 5

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Macomb MI 3 1 1.2 9874 1140 111
Wayne MI 1 2 1.1 27358 1550 175
Oakland MI 2 3 1.1 14816 1180 117
Kent MI 4 4 1.0 7194 1120 63
Gogebic MI 47 5 1.5 96 620 7
Ottawa MI 9 6 1.2 1744 610 22
Shiawassee MI 28 7 1.5 327 480 4
Genesee MI 5 12 1.0 3509 860 30
Washtenaw MI 6 20 0.8 2916 800 19
Saginaw MI 8 26 0.7 1860 960 16
Jackson MI 7 33 0.6 2417 1520 10
WI
county ST case rank severity R_e cases cases/100k daily cases
Barron WI 28 1 2.3 273 600 33
Wood WI 30 2 1.7 240 330 13
Douglas WI 38 3 1.7 130 300 7
Dodge WI 13 4 1.5 690 790 14
Walworth WI 8 5 1.4 1239 1200 32
Monroe WI 32 6 1.6 220 480 7
Milwaukee WI 1 7 0.9 19766 2070 238
Racine WI 5 9 1.2 3226 1650 52
Dane WI 2 11 1.1 4183 790 55
Waukesha WI 4 12 1.0 3694 930 105
Brown WI 3 17 1.0 4001 1540 37
Kenosha WI 6 21 0.9 2482 1470 33
Outagamie WI 9 22 1.0 1104 600 23
Rock WI 7 36 0.8 1504 930 12

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
St. Louis MN 18 1 1.8 420 210 18
McLeod MN 37 2 1.9 134 370 4
Hennepin MN 1 3 1.0 17784 1440 226
Dakota MN 3 4 1.1 3938 940 74
Ramsey MN 2 5 1.0 6865 1270 90
Crow Wing MN 27 6 1.4 207 320 8
Scott MN 9 7 1.2 1363 950 30
Anoka MN 4 8 1.0 3334 960 49
Washington MN 6 9 1.0 1887 740 33
Stearns MN 5 10 1.1 2818 1800 15
Olmsted MN 8 11 1.1 1621 1060 17
Nobles MN 7 32 0.9 1745 7990 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.2 4222 2260 31
Lincoln SD 4 2 1.3 573 1040 15
Brown SD 5 3 1.4 409 1050 5
Union SD 6 4 1.1 198 1300 3
Pennington SD 2 5 0.9 826 760 7
Brookings SD 8 6 1.1 119 350 2
Clay SD 9 7 1.0 117 840 2
Codington SD 7 11 0.6 120 430 1
Beadle SD 3 12 0.4 589 3210 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Burleigh ND 2 1 1.0 976 1040 31
Stark ND 6 2 1.3 194 630 8
Cass ND 1 3 0.9 2931 1680 19
Benson ND 11 4 1.0 97 1410 6
Richland ND 12 5 1.0 94 580 4
Mountrail ND 8 6 1.1 115 1130 2
Morton ND 4 7 0.8 277 910 7
Ward ND 7 9 0.8 182 260 5
Stutsman ND 9 10 0.9 114 540 3
Williams ND 5 11 0.7 233 680 5
Grand Forks ND 3 12 0.6 629 890 5

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
Hartford CT 3 1 1.0 12687 1420 44
Fairfield CT 1 2 0.9 17860 1890 50
Tolland CT 7 3 1.2 1027 680 8
Windham CT 8 4 1.2 698 600 5
New Haven CT 2 5 0.8 13061 1520 18
New London CT 5 6 1.1 1408 520 5
Litchfield CT 4 7 0.8 1606 880 5
Middlesex CT 6 8 0.5 1380 840 1
MA
county ST case rank severity R_e cases cases/100k daily cases
Essex MA 3 1 1.4 17362 2220 64
Suffolk MA 2 2 1.3 21340 2700 73
Middlesex MA 1 3 1.2 25876 1620 85
Norfolk MA 5 4 1.3 10340 1480 58
Worcester MA 4 5 1.2 13412 1630 49
Bristol MA 6 6 1.1 9129 1630 36
Hampden MA 8 7 1.1 7462 1590 27
Plymouth MA 7 8 1.1 9121 1780 20
Barnstable MA 9 10 0.8 1764 830 9
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.0 14630 2310 68
Kent RI 2 2 1.1 1433 870 9
Newport RI 4 3 1.2 383 460 2
Bristol RI 5 4 0.9 307 630 2
Washington RI 3 5 0.9 593 470 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
Ulster NY 13 1 1.6 2039 1140 16
New York City NY 1 2 0.9 230418 2730 283
Dutchess NY 9 3 1.4 4516 1540 14
Suffolk NY 2 4 1.0 43357 2910 66
Nassau NY 3 5 1.1 43317 3190 52
Fulton NY 28 6 1.7 283 530 2
Erie NY 7 7 1.1 8574 930 41
Monroe NY 8 9 1.0 4766 640 26
Westchester NY 4 10 0.9 36004 3720 33
Orange NY 6 11 1.1 11116 2940 14
Rockland NY 5 17 1.0 13882 4290 7

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Rutland VT 4 1 1.0 90 150 1
Chittenden VT 1 2 0.3 720 440 1
Franklin VT 2 3 0.5 118 240 0
Bennington VT 5 4 0.4 85 240 0
Windham VT 3 5 0.0 102 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
York ME 2 1 1.1 656 320 5
Cumberland ME 1 2 0.8 2054 710 6
Penobscot ME 5 3 1.0 148 100 2
Kennebec ME 4 4 0.9 168 140 2
Androscoggin ME 3 5 0.5 554 520 2
NH
county ST case rank severity R_e cases cases/100k daily cases
Rockingham NH 2 1 1.1 1636 540 7
Hillsborough NH 1 2 0.9 3771 920 16
Cheshire NH 7 3 1.2 91 120 1
Strafford NH 4 4 0.9 331 260 2
Belknap NH 5 5 1.0 108 180 1
Carroll NH 8 6 0.8 90 190 1
Merrimack NH 3 7 0.7 460 310 1
Grafton NH 6 8 0.4 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Anderson SC 14 1 1.3 2075 1060 62
Beaufort SC 8 2 1.1 3673 2010 100
Richland SC 4 3 1.1 8006 1960 145
Florence SC 10 4 1.2 3027 2180 86
Aiken SC 16 5 1.2 1587 950 50
Darlington SC 21 6 1.2 1091 1620 35
Georgetown SC 18 7 1.3 1294 2100 29
Greenville SC 2 8 0.9 10251 2060 124
Charleston SC 1 10 0.9 11629 2950 135
Horry SC 3 11 0.9 8147 2540 93
Spartanburg SC 7 15 1.0 3744 1240 47
Berkeley SC 6 19 0.9 3877 1850 58
Lexington SC 5 21 0.8 4666 1630 64
York SC 9 23 0.9 3182 1230 51
NC
county ST case rank severity R_e cases cases/100k daily cases
Alleghany NC 88 1 2.5 83 760 6
McDowell NC 50 2 1.6 610 1350 26
Onslow NC 36 3 1.4 940 480 40
Davie NC 60 4 1.6 371 880 12
Lee NC 30 5 1.5 1192 1980 21
Nash NC 34 6 1.4 1043 1110 29
Johnston NC 6 7 1.2 3069 1610 61
Mecklenburg NC 1 9 0.9 20933 1990 238
Wake NC 2 10 0.9 11196 1070 158
Cumberland NC 9 11 1.1 2752 830 66
Durham NC 3 16 1.0 5894 1920 57
Guilford NC 4 17 0.9 5236 1000 73
Union NC 8 18 1.0 2810 1240 46
Forsyth NC 5 19 1.0 4935 1330 54
Gaston NC 7 26 0.9 3032 1400 51

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Yellowstone MT 1 1 1.3 1091 690 37
Big Horn MT 3 2 1.4 345 2580 20
Flathead MT 5 3 1.3 264 270 13
Missoula MT 4 4 1.2 268 230 9
Lewis and Clark MT 8 5 1.2 133 200 5
Gallatin MT 2 6 0.7 881 840 14
Cascade MT 7 7 0.8 150 180 5
Lake MT 6 8 0.8 172 580 4
Madison MT 9 9 0.9 78 950 2
WY
county ST case rank severity R_e cases cases/100k daily cases
Uinta WY 4 1 1.3 263 1280 4
Laramie WY 2 2 1.1 473 480 8
Teton WY 3 3 0.9 360 1560 11
Fremont WY 1 4 0.9 476 1190 4
Sweetwater WY 5 5 0.7 249 560 4
Natrona WY 6 6 0.7 220 270 3
Park WY 8 7 0.8 119 410 2
Campbell WY 7 8 0.7 119 250 2
Lincoln WY 9 11 0.2 93 490 0
ID
county ST case rank severity R_e cases cases/100k daily cases
Bonneville ID 5 1 1.2 751 670 38
Ada ID 1 2 0.9 8113 1820 145
Canyon ID 2 3 0.9 4910 2310 112
Kootenai ID 3 4 1.0 1606 1050 43
Elmore ID 14 5 1.3 210 790 9
Jerome ID 9 6 1.2 428 1830 7
Jefferson ID 18 7 1.2 145 520 8
Twin Falls ID 4 8 0.9 1235 1480 22
Cassia ID 7 10 1.0 480 2030 9
Minidoka ID 8 17 0.7 438 2120 7
Blaine ID 6 22 0.7 571 2600 1

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Preble OH 55 1 1.8 166 400 8
Lucas OH 4 2 1.2 4869 1130 119
Perry OH 73 3 1.8 107 300 6
Franklin OH 1 4 0.9 17184 1350 208
Fayette OH 76 5 1.7 95 330 5
Cuyahoga OH 2 6 0.9 12663 1010 143
Hamilton OH 3 7 1.0 9131 1120 93
Montgomery OH 5 8 1.0 4056 760 78
Summit OH 6 11 1.0 3268 600 50
Butler OH 8 17 1.0 2697 710 41
Mahoning OH 9 36 0.9 2404 1040 19
Marion OH 7 38 1.1 2875 4400 8
IL
county ST case rank severity R_e cases cases/100k daily cases
Bureau IL 44 1 2.0 143 430 13
Effingham IL 53 2 1.9 106 310 9
Cook IL 1 3 1.1 106677 2040 637
Peoria IL 14 4 1.4 1320 720 50
Tazewell IL 26 5 1.6 370 280 17
Kane IL 4 6 1.2 9271 1750 72
Morgan IL 39 7 1.7 196 570 7
Will IL 5 10 1.1 8594 1250 73
DuPage IL 3 11 1.0 11459 1230 90
St. Clair IL 6 14 1.0 3917 1490 65
Lake IL 2 16 1.0 12012 1710 82
Madison IL 9 19 1.0 2200 830 54
McHenry IL 8 29 0.9 2970 960 32
Winnebago IL 7 33 0.9 3684 1290 24
IN
county ST case rank severity R_e cases cases/100k daily cases
Carroll IN 65 1 2.3 140 700 4
Marion IN 1 2 1.1 14868 1570 155
Vigo IN 34 3 1.4 467 430 17
Madison IN 14 4 1.4 855 660 14
Allen IN 4 5 1.2 3625 980 40
St. Joseph IN 5 6 1.1 3125 1160 50
Clark IN 13 7 1.2 1094 950 27
Vanderburgh IN 9 11 1.0 1714 950 43
Hamilton IN 6 12 1.0 2520 800 41
Hendricks IN 7 17 1.1 1766 1100 16
Lake IN 2 19 0.8 7092 1460 54
Elkhart IN 3 21 0.9 4682 2300 38
Cass IN 8 59 0.7 1755 4610 5

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Johnson TN 73 1 3.2 157 880 23
Lake TN 23 2 2.9 811 10780 19
Henry TN 64 3 1.9 218 680 21
Madison TN 20 4 1.7 883 900 49
Weakley TN 58 5 1.8 265 790 22
Benton TN 84 6 2.0 96 600 10
Shelby TN 1 7 1.2 21736 2320 442
Knox TN 5 14 1.0 4176 920 140
Davidson TN 2 18 0.8 21656 3170 257
Sevier TN 9 22 1.1 1728 1790 51
Rutherford TN 3 34 0.9 6087 1980 101
Williamson TN 6 37 1.0 3234 1480 52
Hamilton TN 4 42 0.8 5659 1580 85
Wilson TN 8 51 0.9 2071 1560 33
Sumner TN 7 65 0.8 3178 1770 40
KY
county ST case rank severity R_e cases cases/100k daily cases
Mercer KY 68 1 2.8 77 360 9
Jefferson KY 1 2 1.0 7172 930 151
Whitley KY 46 3 1.6 136 380 8
Calloway KY 37 4 1.6 187 480 7
Fayette KY 2 5 1.1 3295 1030 71
Clark KY 40 6 1.6 156 430 5
Graves KY 13 7 1.3 512 1370 13
Christian KY 9 9 1.3 583 810 15
Warren KY 3 12 0.9 2433 1920 32
Boone KY 5 15 1.0 1016 790 14
Kenton KY 4 19 0.9 1315 800 19
Daviess KY 6 22 1.0 713 710 10
Shelby KY 7 23 1.0 710 1520 8
Muhlenberg KY 8 59 0.5 630 2030 2

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
Taney MO 17 1 1.7 439 800 33
Jackson MO 4 2 1.4 3502 510 130
St. Louis MO 1 3 1.1 13478 1350 355
Cole MO 22 4 1.5 317 410 14
St. Louis city MO 2 5 1.1 4664 1500 102
Boone MO 7 6 1.3 1250 710 33
Cooper MO 51 7 1.6 100 570 8
St. Charles MO 3 11 1.0 3715 950 96
Jefferson MO 5 16 1.0 1457 650 38
Greene MO 6 20 0.9 1284 450 33
Buchanan MO 9 44 0.9 1062 1190 6
Jasper MO 8 47 0.6 1219 1020 13
AR
county ST case rank severity R_e cases cases/100k daily cases
Greene AR 26 1 2.2 369 830 30
Chicot AR 20 2 1.8 565 5220 40
Mississippi AR 18 3 1.7 769 1800 40
Independence AR 25 4 1.4 378 1010 28
Logan AR 45 5 1.7 153 700 6
Sebastian AR 4 6 1.3 1780 1400 59
Ashley AR 32 7 1.4 250 1220 13
Hot Spring AR 5 9 1.5 1458 4350 10
Jefferson AR 6 11 1.1 1350 1920 27
Pulaski AR 2 13 0.8 4875 1240 73
Benton AR 3 15 0.9 4543 1750 45
Crittenden AR 8 17 1.0 1210 2470 20
Washington AR 1 18 0.8 6010 2630 48
Faulkner AR 7 22 0.9 1214 990 20
Pope AR 9 27 0.8 1206 1890 21

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 1236.3 seconds to compute.
2020-08-03 06:40:45

version history

Today is 2020-08-03.
75 days ago: Multiple states.
67 days ago: \(R_e\) computation.
64 days ago: created color coding for \(R_e\) plots.
59 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
59 days ago: “persistence” time evolution.
52 days ago: “In control” mapping.
52 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
44 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
39 days ago: Added Per Capita US Map.
37 days ago: Deprecated national map.
33 days ago: added state “Hot 10” analysis.
28 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
26 days ago: added per capita disease and mortaility to state-level analysis.
14 days ago: changed to county boundarieson national map for per capita disease.
9 days ago: corrected factor of two error in death trend data.
5 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.